\defmodule {ContinuousDistributionMulti}

Classes implementing continuous multi-dimensional distributions should inherit 
from this class. Such distributions are characterized by a \emph{density}
 function $f(x_1, x_2, \ldots, x_d)$;
thus the signature of a \texttt{density} method is supplied here.
All array indices start at 0.

\bigskip\hrule

\begin{code}
\begin{hide}
/*
 * Class:        ContinuousDistributionMulti
 * Description:  mother class for continuous multidimensional distributions 
 * Environment:  Java
 * Software:     SSJ 
 * Copyright (C) 2001  Pierre L'Ecuyer and Universite de Montreal
 * Organization: DIRO, Universite de Montreal
 * @author       
 * @since

 * SSJ is free software: you can redistribute it and/or modify it under
 * the terms of the GNU General Public License (GPL) as published by the
 * Free Software Foundation, either version 3 of the License, or
 * any later version.

 * SSJ is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.

 * A copy of the GNU General Public License is available at
   <a href="http://www.gnu.org/licenses">GPL licence site</a>.
 */
\end{hide}
package umontreal.iro.lecuyer.probdistmulti;
\begin{hide}
import umontreal.iro.lecuyer.util.PrintfFormat;
import umontreal.iro.lecuyer.util.Num;
\end{hide}

public abstract class ContinuousDistributionMulti\begin{hide} {
   protected int dimension;
\end{hide}


   public abstract double density (double[] x);
\end{code}
\begin{tabb} Returns $f(x_1, x_2, \ldots, x_d)$, the probability density of
  $X$ evaluated at the point
 $x$, where $x = \{x_1, x_2, \ldots, x_d\}$. The convention is that 
  $\texttt{x[i-1]} = x_i$.
\end{tabb}
\begin{htmlonly}
   \param{x}{value at which the density is evaluated}
   \return{density function evaluated at \texttt{x}}
\end{htmlonly}
\begin{code}

   public int getDimension()\begin{hide} {
      return dimension;
   }\end{hide}
\end{code}
\begin{tabb}
   Returns the dimension $d$ of the distribution.
\end{tabb}
\begin{code}

   public abstract double[] getMean();
\end{code}
\begin{tabb}
   Returns the mean vector of the distribution, defined as $\mu_{i} = E[X_i]$.
\end{tabb}
\begin{code}

   public abstract double[][] getCovariance();
\end{code}
\begin{tabb}
   Returns the variance-covariance matrix of the distribution, defined as\\
   $\sigma_{ij} = E[(X_i - \mu_i)(X_j - \mu_j)]$.
\end{tabb}
\begin{code}

   public abstract double[][] getCorrelation();
\end{code}
\begin{tabb}
   Returns the correlation matrix of the distribution, defined as
      $\rho_{ij} = \sigma_{ij}/\sqrt{\sigma_{ii}\sigma_{jj}}$.
\end{tabb}
\begin{code}\begin{hide}
}\end{hide}
\end{code}
